# Endpoints

DataBridge's API is organized into several logical paths:

## [Ingest](https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/ingest)

Endpoints for ingesting and managing documents:

* Text document ingestion
* File document ingestion
* Document metadata management

## [Search](https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/search)

Endpoints for semantic search functionality:

* Document chunk search
* Document listing and retrieval
* Document metadata search

## [Query](https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/query)

Endpoints for AI-powered query operations:

* AI completions with context
* RAG-based question answering
* Context-aware responses

## [Cache](https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/cache)

*Coming soon*

## [Response Models](https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/models)

Common data models used across the API:

* Document models
* Search result models
* Query response models

## Response Models

### Document

```python
class Document:
    external_id: str
    owner: Dict[str, str]
    content_type: str
    filename: Optional[str]
    metadata: Dict[str, Any]  # user-defined metadata
    storage_info: Dict[str, str]  # storage backend info
    system_metadata: Dict[str, Any]  # creation date, version, etc.
    additional_metadata: Dict[str, Any]  # e.g., frame descriptions and transcripts for videos
    access_control: Dict[str, List[str]]  # readers, writers, admins
    chunk_ids: List[str]
```

### ChunkResult

```python
class ChunkResult:
    content: str
    score: float
    document_id: str  # external_id
    chunk_number: int
    metadata: Dict[str, Any]
    content_type: str
    filename: Optional[str]
    download_url: Optional[str]

    def augmented_content(self, doc: DocumentResult) -> str:
        """Get augmented content for video chunks with frame/transcript info"""
```

### DocumentResult

```python
class DocumentResult:
    score: float  # Highest chunk score
    document_id: str  # external_id
    metadata: Dict[str, Any]
    content: DocumentContent  # type and value fields
    additional_metadata: Dict[str, Any]  # e.g., frame descriptions and transcripts
```

### DocumentContent

```python
class DocumentContent:
    type: Literal["url", "string"]  # Content type
    value: str  # URL or actual content
    filename: Optional[str]  # Required for URL type, None for string type
```

### CompletionResponse

```python
class CompletionResponse:
    completion: str
    usage: TokenUsage  # completion_tokens, prompt_tokens, total_tokens
```